Accuracy and uncertainty of spatial patterns of soil organic matter

Abstract

Accurate quantification of the spatial patterns of soil organic matter (SOM) is essential for both SOM management and for the application of SOM models. The objective of this study is to determine whether elevation could be used to increase the accuracy of spatial predictions and the corresponding prediction uncertainty of soil organic matter. The sequential Gaussian simulation (SGS) and sequential Gaussian co-simulation (SGCS) algorithms were compared with respect to the accuracy of predictions as well as to the uncertainty inherent in the spatial prediction of soil organic matter. The SGS algorithm accounted for only the SOM data. The SGCS accounted for both SOM data and intensive elevation data. The root mean square errors revealed that the more accurate simulations were those accounting for intensive elevation inform ation by the SGCS method for the two areas compared with SGS. As regards modelling local uncertainty, SGCS performed better at modelling prediction uncertainty than SGS. In addition, the results of assessing the standard deviation confirmed that the exhaustive elevation data could be used to reduce the spatial uncertainty of SOM by SGCS compared with the SGS algorithm.

Accuracy and uncertainty assessment on geostatistical simulation of soil salinity in a coastal farmland using auxiliary variable

“…The possible explanation was that more information was included in the simulation when using intensive EM survey data as the covariate to predict the sparser soil salinity. Actually, the stronger the correlation between target variable and auxiliary variable, the more benefits accrued from using the more intensive auxiliary data and SGCS algorithm (Chai et al 2007). Other than simulation methods, this result was widely found in the interpolation procedure (Eldeiry and Garcia 2010).…”

Modelagem e quantificação da incerteza espacial do potássio disponível no solo por simulações estocásticas

“…em que n é o número de valores utilizados na validação, no presente trabalho n=63; z(x i ) é o valor da propriedade no ponto i; e z ꞈ (x i ) é o valor estimado da propriedade no ponto i. Menores valores de REQM estão relacionados à maior acurácia nas predições (Chai et al, 2007).…”

“…A simulação sequencial gaussiana (SSG) e a simulação sequencial indicatriz (SSI) são os tipos mais comuns de simulação estocástica. A SSG é a mais utilizada em estudos sobre propriedades químicas do solo (Bourennane et al, 2007;Chai et al, 2007;Delbari et al, 2009;Zhao et al, 2009;Sun et al, 2012), enquanto a SSI (Delbari et al, 2010) é frequentemente utilizada na área ambiental (Juang et al, 2004;Zhao et al, 2005). Ao contrário da KO, os modelos de simulação estocástica reproduzem a variabilidade espacial dos dados, o que elimina o efeito de suavização.…”

A New Method for Spatial Health Risk Assessment of Pollutants

“…In the latest method, despite the SGS, variograms, and crossvariograms should be fitted with a linear model of coregionalization (LMC) to satisfy the positive definiteness condition [17]. In this technique, simple collocated cokriging is used instead of simple kriging to implement the simulations [5] and the conditioning data consists of primary and secondary data along with the previously simulated values. In both SGS and SGCS, the simulated values are back-transformed using the inverse of the Gaussian anamorphosis function used for normal score transformation of original data.…”

“…There have been multiple applications of sequential Gaussian co-simulation for the study of spatial pattern of soil attributes including soil organic matter [5], bioavailable parts of phosphorus and potassium [6], and soil salinity [7]. However, even though, there is few case studies about its application for the assessment of contaminated sites.…”